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     "source": [
      "# Index\n",
      "\n",
      "This is the index of notebooks for my portions of the ESAC statistics workshop, October 27-31 at the ESAC outside Madrid, Spain. The source of this material can be found at http://github.com/jakevdp/ESAC-stats-2014.\n",
      "\n",
      "You can see the workshop details & agenda here: http://www.cosmos.esa.int/web/esac-science-faculty/esac-statistics-workshop-2014."
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "---\n",
      "\n",
      "## Monday\n",
      "\n",
      "- Morning Session:\n",
      "  + [Model Fitting I (frequentist)](01.1-Frequentist-Model-Fitting.ipynb)\n",
      "  + [Breakout](01.2-Model-Fitting-Breakout.ipynb) (Solution notebook [here](Solution-01.2.ipynb))\n",
      "  \n",
      "- Afternoon Session:\n",
      "  + [Model Fitting II (Bayesian)](02.1-Bayesian-Model-Fitting.ipynb)\n",
      "  + [Breakout](02.2-Afternoon-Breakout.ipynb) (Solution notebook [here](Solution-02.2.ipynb))"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "---\n",
      "\n",
      "## Thursday\n",
      "- Afternoon Session:\n",
      "  + [Introduction to Machine Learning with Scikit-Learn](03.1-Scikit-Learn-Intro.ipynb)\n",
      "  + [Breakout](03.2-Machine-Learning-Breakout.ipynb) (Solutions notebook [here](Solution-03.2.ipynb))"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "---\n",
      "## Friday\n",
      "- Morning Session:\n",
      "  + [Classification: SVMs in-depth](04.1-Classification-SVMs.ipynb)\n",
      "  + [Regression: Trees & Forests in-depth](04.2-Regression-Forests.ipynb)\n",
      "  + [Model Validation & Testing](04.3-Validation.ipynb)\n",
      "  + [Breakout](04.4-Validation-Breakout.ipynb) (Solutions notebook [here](Solution-04.4.ipynb))\n",
      "  \n",
      "- Afternoon Session:\n",
      "  + [Dimensionality Reduction: PCA in-depth](05.1-Dimensionality-PCA.ipynb)\n",
      "  + [Clustering: KMeans in-depth](05.2-Clustering-KMeans.ipynb)\n",
      "  + [Density Estimation: Gaussian Mixtures in-depth](05.3-Density-GMM.ipynb)\n",
      "  + [Breakout](05.4-Unsupervised-Breakout.ipynb) (There aren't really *solutions* to this per se... it's more of an open-ended exercise. To see some things I've done with this dataset and these tools, you can refer to [our textbook](http://press.princeton.edu/titles/10159.html), particularly [Figures 10.20-21](http://www.astroml.org/book_figures/chapter10/fig_LINEAR_clustering.html), [Figure 10.22](http://www.astroml.org/book_figures/chapter10/fig_LINEAR_GMMBayes.html), and [Figure 10.23](http://www.astroml.org/book_figures/chapter10/fig_LINEAR_SVM.html))"
     ]
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